Algo no-go?

Are there algos that can deliver the goods in those corners of the market where the rarest and most illiquid commodities are traded? Shayla Walmsley discusses the execution challenges in a trading environment where, if there were still pits, they would be full of armchairs.

Complex market conventions plus multiple pricing factors plus low
volumes; three factors that add together to give you - no algo.
That, in short, is what you'll find if you're looking for
execution algos for illiquid commodities.

Those for more liquid commodities - for example, energy
commodities like crude and natural gas - have been around a
while. But it's only relatively recently that commodities markets
have become fully open to electronic trading - a prerequisite for
algo trading - and, perhaps oddly, the attraction of the rarities
hasn't triggered the evolution of correspondingly 'inquisitive'
algorithms.

Auto may be a prerequisite for algo, but it is not in itself
sufficient. Try finding traders already using execution algos for
illiquid commodities and you're likely draw a blank. Lack of
appetite? Possibly. But more probable is the sheer diversity of
variables they need to factor in. These, at least, are becoming
relatively clear for the pioneer in the field.

Compared with those for illiquid securities, execution algos for
illiquid commodities simply have to do more. For one thing, they
need to measure both volumes and volatility: more volatile
(because less liquid) commodities require models that build
signals to protect against losses.

"...if there were still trading pits, they would be full of
armchairs"

"We haven't had much demand for something like that," says John
Hyde, execution consultant at Bank of America Merrill Lynch -
though he points out that the bank's Ambush algo "discreetly
probes the market and executes trades when urgency is high and
impact is of paramount concern".

Some of this complexity comes from the fact that behind these
commodities are physical assets. [Even the terms 'liquid' and
'illiquid', as applied to commodities, are of questionable
accuracy, according to Luke Jemmett, who is responsible for the
market in energy-related commodities, including carbon and wet
and dry freight, for interdealer broker GFI. Jemmett points to a
relatively liquid German power market compared with emerging
Eastern European markets that are significantly less liquid.]

Either way, these are not bonds, as David Allen, commodities
sales and trading manager at AVM LP, puts it. A natural gas
operation, say, is one component of an interconnected system
linked to multiple factors and prices across regions - the price
of transportation, insurance and spot fuel, weather-implied
demand, the forward price on the curve, the demands of power
plant and retail customers.

Luke Jemmett

"Traders need to take into account cash versus physical
settlement, delivery location, grade, expiry date and the rest,"
says Allen. "In the case of the US, if the delivery location of
the gas is some point in Louisiana rather than in southern
California, you have different geographies, infrastructure,
weather and price. In contrast, in the UK, gas is delivered and
indexed to a national balancing point in a virtual location. The
devil is always in the detail. Market convention is often very
important.

"Most trading operations aren't comfortable modelling these
systems and building such an algo in-house because it involves
too many complex and distinct variables," he adds. "These systems
are not fungible with other financial non-tangible systems."

The lack of fungibility has created another problem in the
development of execution algos for illiquid commodities, suggests
Allen. Pure tech developers tend to see it as an adjunct to
existing asset classes, rather than a qualitatively distinct
algo.

"Algorithms are hard to write," he says. "The danger is that, if
left unchecked, technology purists might treat execution algos
for commodities like any other code, with the naive assumption
that an asset is an asset; that if you can track it, chart it,
and data aggregate it, you can trade it; and that market
conventions are roughly the same for any market."

What they need instead, he claims, is an appreciation for the
scheduling and operational detail, decision support tools and
time series programmes necessary for training and support.

Ben Jackson

"Investing in commodities for me could mean acquiring physical
power plants - buying, operating, restructuring and, potentially,
later on selling the physical asset. The problem is that most
people active in the market come from a financial background, not
a true infrastructure background. If you were to do a rough
count, you'd find that 90% of commodity algo traders had
absolutely no background in physical commodities."

Even if it's not possible to develop a commodity-execution algo
by tweaking, say, a fixed income one, say, Allen points to
cross-asset systems that can identify and monetise differentials
between interrelated commodities, where triangulation may exist
with equity and FX markets.

"As beta becomes more expensive and individual commodity markets
resume exhibiting their own unique price and volatility profiles,
quant traders will be forced to explore higher order implied
spreads between assets," says Allen.

One advantage of bringing algos in-house is that they're tailored
to the needs of (potentially multiple) markets, according to
Günter Tschiderer, a fund manager with Sigma Commodities, a
BNP Paribas Investment Partners commodity business. "I'm very
open to algo execution. Why not?" he says, though he adds that
the house does not allow him to trade in illiquid commodities.

In any case, execution algos have their work cut out. The
technology needs to be capable of modelling "down to a tick",
says Ben Jackson, senior executive vice-president of Sungard's
energy and commodities business. "In terms of execution
platforms, speed is critical, as is the ability to mitigate
post-trade risk. The challenge is to understand risk in multiple
dimensions, such as position, margin, P&L and market risk,
and understand this instantly."

Speed is one issue; alerting the market is another. These are
trades that keep their heads down.

One problem algos for all illiquid assets share is the potential
for market impact. This makes many forms of more aggressive algos
designed for the more liquid equities and exchange-traded futures
markets unsuitable. At the same time, argues Richard Bentley,
general manager for capital markets at Progress Software, more
passive algorithms such as electronic eyes ('snipers'), which
take smaller quantities and then back off and wait for the market
to repopulate, can be used effectively in illiquid markets.

Richard Bentley

GTI provides windows when customers can come in and place a price
without others seeing it. In theory, this will encourage
participants to place orders on the open market rather than
trading bilaterally or via dark pools. It's only when the trade
is executed that it becomes apparent to the market.

The problem is timing risk: the longer the period you execute
over, the more potential for the market to move away from you. In
any case, illiquid markets require a pre-trade impact analysis to
understand how the trade will move the market.

Ray Murphy, chief risk officer at Nautical Capital, believes that
all this leads to continued reliance on human intervention.
Placing and cancelling trades as you try to gain an edge on the
wide bid/ask spreads indicated on-screen results in "a lot of
phantom liquidity", which can play havoc with algo trading
systems.

"That is part of the reason I have always tried to manually
handle at least portions of the trading," says Murphy. "I can be
more specific in both the price and timing of execution." He adds
that most of the algorithmic trading done at Nautical is based on
end-of-day pricing. The availability of trade settlement orders
makes these types of algorithmic systems much easier to execute,
he claims.

GFI traditionally used a hybrid model that combines technology
with voice. If you use an electronic system, there may be spread
between bid and offer, says Jemmett. "Technology is good because
it aids transparency and liquidity but there are things you can't
do. Depending on the market, the split between electronic and
voice trades is slightly different."

Doug Hepworth

In the meantime, to bring illiquid commodities even close to the
securities market, you need arbitrage, says Douglas Hepworth,
research director of Gresham Investment Management. The problem
is that "tangible" (that is, illiquid) commodity markets are not
arbitrage markets.

If you have futures versus physical assets or versus other
related futures, electronic trading makes it easier to capture
small discrepancies. If the futures are cheap, you buy the
futures and sell the component stocks; when they're rich, pari
passu, you sell the futures and buy the stocks.

"This does not work in the tangible commodities markets,"
Hepworth points out. "If you see discrepancies in cattle versus
cattle futures, you could buy cattle and sell futures, but with
much greater cost and more complexity. And on the other side, you
can't short live cattle. That closes off the normal arbitrage
flow; it is at best an asymmetric arbitrage."

A potential solution to the problem of illiquidity is to build
liquidity. This sounds tautological but it acknowledges the point
made by Jackson - that the technology itself isn't enough:
optimal execution depends as much on the modelling maturity of
the algo trader.

At the simplest strategic level, there are arbitrage
opportunities from a single exchange, with spread contracts
versus futures and winter/summer contracts for natural gas.

A second model comprises execution across multiple exchanges,
involving multiple data sources - for example, for spreads versus
futures on fungible contracts that trade on NYMEX and ICE. In
this case, the technology and data requirements introduce another
level of complexity.

The final model involves markets with less liquidity. "We're
seeing some of our customers looking at these markets," says
Jackson. "If you look at heating oil contracts, the liquidity
hasn't been there. So there has been starting and stopping of
strategies here. It's unclear whether we'll see the volumes but
we're definitely seeing moves from our customers, as well as
exchanges pushing customers into this area."

Algo trading introduces liquidity into these markets. "What you
have in every market is that it's difficult to be first - to put
your strategy against bringing that kind of liquidity into the
market. If you take emissions, you have listings on multiple
exchanges, which presents arbitrage opportunities. But you
haven't seen sufficient volumes or volatility yet."

If not now …

There are some important distinctions when it comes to
execution algos for illiquid commodities. The first is between
traders who would like to use them and those who couldn't care
less. It's a pretty even split.

It seems reasonable to believe that there will eventually be an
execution algo for pretty much every tradable asset class. But
illiquid commodities are close to the bottom of the list. One
buy-side trader identified "at least five" algos he wanted to
see developed before illiquid commodities - including Treasury
futures and long bonds.

"Equities will always have more client demand but developing
algos for futures rounds out the offering," says Hyde. "We
looked at the trend and saw the open-outcry model going by the
wayside."

Yet demand will probably increase for a commodities algo with a
boosted investor appetite for the asset class. Jackson points
to a growth in algo trading within the context of the growth in
futures markets compared to a decline in equities, pointing out
that the CME recently posted year-on-year gains of 90% and ICE,
12%.

Moreover, some of the most illiquid commodities have been
the ones to recover most quickly, from a price perspective,
after 2008. The speediest recoveries came with some of the most
esoteric: palladium, vegetable oils, rubber and other softs -
the ones Allen describes as "very unattractive commodities from
a media standpoint - the equivalent of the redheaded
stepchildren of oil and gold".